Case Study: Deontological Ethics in NLP

Shrimai Prabhumoye, Brendon Boldt, Ruslan Salakhutdinov, Alan W Black

Abstract
Recent work in natural language processing (NLP) has focused on ethical challenges such as understanding and mitigating bias in data and algorithms; identifying objectionable content like hate speech, stereotypes and offensive language; and building frameworks for better system design and data handling practices. However, there has been little discussion about the ethical foundations that underlie these efforts. In this work, we study one ethical theory, namely deontological ethics, from the perspective of NLP. In particular, we focus on the generalization principle and the respect for autonomy through informed consent. We provide four case studies to demonstrate how these principles can be used with NLP systems. We also recommend directions to avoid the ethical issues in these systems.
Anthology ID:
2021.naacl-main.297
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Editors:
Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3784–3798
Language:
URL:
https://aclanthology.org/2021.naacl-main.297
DOI:
10.18653/v1/2021.naacl-main.297
Bibkey:
Cite (ACL):
Shrimai Prabhumoye, Brendon Boldt, Ruslan Salakhutdinov, and Alan W Black. 2021. Case Study: Deontological Ethics in NLP. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3784–3798, Online. Association for Computational Linguistics.
Cite (Informal):
Case Study: Deontological Ethics in NLP (Prabhumoye et al., NAACL 2021)
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